diff --git a/src/lightning/pytorch/profilers/simple.py b/src/lightning/pytorch/profilers/simple.py
index 50b37624bb944..74d68039d255b 100644
--- a/src/lightning/pytorch/profilers/simple.py
+++ b/src/lightning/pytorch/profilers/simple.py
@@ -19,7 +19,7 @@
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple, Union
 
-import numpy as np
+import torch
 
 from lightning.pytorch.profilers.profiler import Profiler
 
@@ -79,16 +79,28 @@ def stop(self, action_name: str) -> None:
 
     def _make_report_extended(self) -> Tuple[_TABLE_DATA_EXTENDED, float, float]:
         total_duration = time.monotonic() - self.start_time
-        report = [
-            (a, np.mean(d), len(d), np.sum(d), 100.0 * np.sum(d) / total_duration)
-            for a, d in self.recorded_durations.items()
-        ]
+        report = []
+
+        for a, d in self.recorded_durations.items():
+            d_tensor = torch.tensor(d)
+            len_d = len(d)
+            sum_d = torch.sum(d_tensor).item()
+            percentage_d = 100.0 * sum_d / total_duration
+
+            report.append((a, sum_d / len_d, len_d, sum_d, percentage_d))
+
         report.sort(key=lambda x: x[4], reverse=True)
         total_calls = sum(x[2] for x in report)
         return report, total_calls, total_duration
 
     def _make_report(self) -> _TABLE_DATA:
-        report = [(action, np.mean(d), np.sum(d)) for action, d in self.recorded_durations.items()]
+        report = []
+        for action, d in self.recorded_durations.items():
+            d_tensor = torch.tensor(d)
+            sum_d = torch.sum(d_tensor).item()
+
+            report.append((action, sum_d / len(d), sum_d))
+
         report.sort(key=lambda x: x[1], reverse=True)
         return report
 
diff --git a/src/lightning/pytorch/utilities/model_summary/model_summary.py b/src/lightning/pytorch/utilities/model_summary/model_summary.py
index cde2ffe20262f..15437ec913d3b 100644
--- a/src/lightning/pytorch/utilities/model_summary/model_summary.py
+++ b/src/lightning/pytorch/utilities/model_summary/model_summary.py
@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Utilities related to model weights summary."""
+
 import contextlib
 import logging
+import math
 from collections import OrderedDict
-from typing import Any, cast, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
-import numpy as np
 import torch
 import torch.nn as nn
 from torch import Tensor
@@ -120,9 +121,7 @@ def layer_type(self) -> str:
     @property
     def num_parameters(self) -> int:
         """Returns the number of parameters in this module."""
-        return sum(
-            cast(int, np.prod(p.shape)) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters()
-        )
+        return sum(math.prod(p.shape) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
 
 
 class ModelSummary:
@@ -392,8 +391,8 @@ def get_human_readable_count(number: int) -> str:
     """
     assert number >= 0
     labels = PARAMETER_NUM_UNITS
-    num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)
-    num_groups = int(np.ceil(num_digits / 3))
+    num_digits = int(math.floor(math.log10(number)) + 1 if number > 0 else 1)
+    num_groups = int(math.ceil(num_digits / 3))
     num_groups = min(num_groups, len(labels))  # don't abbreviate beyond trillions
     shift = -3 * (num_groups - 1)
     number = number * (10**shift)